How can I train a neural network if I don't have enough data?
You can synthetically increase the number of samples. For example with augmentation or unsupervised adaption (Self-training). With augmentation you grant the system way more robustness so i would really recommend this. For example this github. The problem with such small database sizes is that your test-set is also very small and you cannot test properly if your network generalizes well, or just overfits.

You can try transfer learning with another larger network to adapt those feature extractors and use them on ur problem. That may work better than training a new one from scratch with so less labeled images. Hope i could help at least a little, stay tuned.